Python StringIndexer.transform方法代碼示例

# 需要導入模塊: from pyspark.ml.feature import StringIndexer [as 別名]
# 或者: from pyspark.ml.feature.StringIndexer import transform [as 別名]
def run(start1, end1, start2, end2, df, sc, sql_context, is_pred):
    lp_data= get_labeled_points(start1, end2, df, sc, sql_context)
    print lp_data.count()

    labelIndexer = StringIndexer(inputCol="label", outputCol="indexedLabel").fit(lp_data)
    td = labelIndexer.transform(lp_data)
    label2index = {}
    for each in  sorted(set([(i[0], i[1]) for i in td.select(td.label, td.indexedLabel).distinct().collect()]),
                key=lambda x: x[0]):
        label2index[int(each[0])] = int(each[1])
    print label2index

    featureIndexer = \
        VectorIndexer(inputCol="features", outputCol="indexedFeatures", maxCategories=4).fit(lp_data)

    rf = get_model()

    pipeline = Pipeline(stages=[labelIndexer, featureIndexer, rf])

    lp_train = lp_data.filter(lp_data.date3<end1).filter(lp_data.is_labeled == 1)
    model = pipeline.fit(lp_train)
    lp_check = lp_data.filter(lp_data.date2>start2)
    predictions = model.transform(lp_check)
    predictions = val(predictions, label2index, sql_context)

    if is_pred:
        predictions = predictions.filter(predictions.is_labeled ==0).filter(predictions.date2 == get_cur()).sort(predictions.prob.desc())
        dfToTableWithPar(sql_context, predictions, "predictions", get_cur())
        for each in predictions.take(10):
            print each

# 需要導入模塊: from pyspark.ml.feature import StringIndexer [as 別名]
# 或者: from pyspark.ml.feature.StringIndexer import transform [as 別名]

#.........這裏部分代碼省略.........

    print('Loaded and prapared %d entries' % df.count())

    #########
    # keep only needed features
    #########   

    features = ['ADLOADINGTIME',
     'PLACEMENTID',
     'TIMESTAMP',
     'CREATIVETYPE',
     'UA_HARDWARETYPE',
     'UA_VENDOR',
     'UA_MODEL',
     'UA_BROWSER',
     'UA_BROWSERVERSION',
     'FILESJSON',
     'ERRORSJSON',
     'TOPMOSTREACHABLEWINDOWAREA',
     'FILESJSON_SIZE',
     'COMBINEDID',
     'COMBINEDEXTERNALID',
     'PLATFORMCOMBINED',
     'UA_OSCOMB',
     'SDK',
     'EXTERNALADSERVER'
       ]

    df = df.select(features)

    #########
    # Convert categorical features to numerical
    #########   


    featuresCat = [
     'PLACEMENTID',
     'CREATIVETYPE',
     'UA_HARDWARETYPE',
     'UA_VENDOR',
     'UA_MODEL',
     'UA_BROWSER',
     'UA_BROWSERVERSION',
     'FILESJSON',
     'ERRORSJSON',
     'COMBINEDID',
     'COMBINEDEXTERNALID',
     'PLATFORMCOMBINED',
     'UA_OSCOMB',
     'SDK',
     'EXTERNALADSERVER'
       ]

    for i in range(len(featuresCat)):

        indexer = StringIndexer(inputCol=featuresCat[i], outputCol='_'+featuresCat[i]).setHandleInvalid("skip").fit(df)
        df = indexer.transform(df).drop(featuresCat[i])
        writer = indexer._call_java("write")
        writer.overwrite().save("indexer_" + featuresCat[i])    

    featuresCat = [ '_' + featuresCat[i] for i in range(len(featuresCat))]    

    features = featuresCat[:]
    features.append('TIMESTAMP')    
    features.append('FILESJSON_SIZE')
    features.append('TOPMOSTREACHABLEWINDOWAREA')


    #########
    # Assemble features
    #########   


    assembler = VectorAssembler(
        inputCols=features,
        outputCol="features")

    df = assembler.transform(df)

    #########
    # Convert to labeled point
    #########   


    lp = (df.select(func.col("ADLOADINGTIME").alias("label"), func.col("features"))
      .map(lambda row: LabeledPoint(row.label, row.features)))
    lp.cache()


    #########
    # Load trained model
    #########
    
    model = RandomForestModel.load(sc, loadModelName)
    
    print('Model loaded!')
    
    predictions = model.predict(lp.map(lambda x: x.features)).collect()
    
    return predictions

# 需要導入模塊: from pyspark.ml.feature import StringIndexer [as 別名]
# 或者: from pyspark.ml.feature.StringIndexer import transform [as 別名]
train_feature_df = feature_df.filter(feature_df['time'] <= split_time)
test_feature_df = feature_df.filter(feature_df['time'] > split_time)

train_feature_df = train_feature_df.drop('time')
test_feature_df = test_feature_df.drop('time')

assembler = VectorAssembler(
    inputCols=list(set(train_feature_df.columns) - set(['result', 'home_name', 'away_name'])),
    outputCol="features")

train_df = assembler.transform(train_feature_df)
test_df = assembler.transform(test_feature_df)

labelIndexer = StringIndexer(inputCol="result", outputCol="indexedResult").fit(feature_df)

train_df = labelIndexer.transform(train_df)
test_df = labelIndexer.transform(test_df)

label_mapping = dict(enumerate(labelIndexer.labels()))
reverse_mapping = {}
for key in label_mapping:
    reverse_mapping[label_mapping[key]] = key


# ## Dimensionality reduction
# 
# Feature selection is not really supported yet in mllib, therefore, we just applied dim reduction using PCA

# In[509]:

pca = PCA(inputCol="features", outputCol="pca", k=15).fit(train_df)